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Guo Z, Song JK, Barbastathis G, Glinsky ME, Vaughan CT, Larson KW, Alpert BK, Levine ZH. Physics-assisted generative adversarial network for X-ray tomography. OPTICS EXPRESS 2022; 30:23238-23259. [PMID: 36225009 DOI: 10.1364/oe.460208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/31/2022] [Indexed: 06/16/2023]
Abstract
X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori, deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Compared with methods with less physics assisting in training, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. The advantages of using a physics-assisted learned prior in X-ray tomography may further enable low-photon nanoscale imaging.
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Karagadde S, Leung CLA, Lee PD. Progress on In Situ and Operando X-ray Imaging of Solidification Processes. MATERIALS (BASEL, SWITZERLAND) 2021; 14:2374. [PMID: 34063314 PMCID: PMC8125014 DOI: 10.3390/ma14092374] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/26/2021] [Accepted: 04/28/2021] [Indexed: 11/29/2022]
Abstract
In this review, we present an overview of significant developments in the field of in situ and operando (ISO) X-ray imaging of solidification processes. The objective of this review is to emphasize the key challenges in developing and performing in situ X-ray imaging of solidification processes, as well as to highlight important contributions that have significantly advanced the understanding of various mechanisms pertaining to microstructural evolution, defects, and semi-solid deformation of metallic alloy systems. Likewise, some of the process modifications such as electromagnetic and ultra-sound melt treatments have also been described. Finally, a discussion on the recent breakthroughs in the emerging technology of additive manufacturing, and the challenges thereof, are presented.
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Affiliation(s)
- Shyamprasad Karagadde
- Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Chu Lun Alex Leung
- Department of Mechanical Engineering, University College London, London WC1E 7JE, UK; (C.L.A.L.); (P.D.L.)
- Research Complex at Harwell, Harwell Campus, Oxfordshire OX11 0FA, UK
| | - Peter D. Lee
- Department of Mechanical Engineering, University College London, London WC1E 7JE, UK; (C.L.A.L.); (P.D.L.)
- Research Complex at Harwell, Harwell Campus, Oxfordshire OX11 0FA, UK
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Gajjar P, Jørgensen JS, Godinho JRA, Johnson CG, Ramsey A, Withers PJ. New software protocols for enabling laboratory based temporal CT. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2018; 89:093702. [PMID: 30278752 DOI: 10.1063/1.5044393] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 08/12/2018] [Indexed: 05/25/2023]
Abstract
Temporal micro-computed tomography (CT) allows the non-destructive quantification of processes that are evolving over time in 3D. Despite the increasing popularity of temporal CT, the practical implementation and optimisation can be difficult. Here, we present new software protocols that enable temporal CT using commercial laboratory CT systems. The first protocol drastically reduces the need for periodic intervention when making time-lapse experiments, allowing a large number of tomograms to be collected automatically. The automated scanning at regular intervals needed for uninterrupted time-lapse CT is demonstrated by analysing the germination of a mung bean (vigna radiata), whilst the synchronisation with an in situ rig required for interrupted time-lapse CT is highlighted using a shear cell to observe granular segregation. The second protocol uses golden-ratio angular sampling with an iterative reconstruction scheme and allows the number of projections in a reconstruction to be changed as sample evolution occurs. This overcomes the limitation of the need to know a priori what the best time window for each scan is. The protocol is evaluated by studying barite precipitation within a porous column, allowing a comparison of spatial and temporal resolution of reconstructions with different numbers of projections. Both of the protocols presented here have great potential for wider application, including, but not limited to, in situ mechanical testing, following battery degradation and chemical reactions.
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Affiliation(s)
- Parmesh Gajjar
- Henry Moseley X-Ray Imaging Facility, School of Materials, The University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom
| | - Jakob S Jørgensen
- Henry Moseley X-Ray Imaging Facility, School of Materials, The University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom
| | - Jose R A Godinho
- Henry Moseley X-Ray Imaging Facility, School of Materials, The University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom
| | - Chris G Johnson
- School of Mathematics, The University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom
| | - Andrew Ramsey
- Nikon Metrology Inc., 12701 Grand River Avenue, Brighton, Michigan 48116, USA
| | - Philip J Withers
- Henry Moseley X-Ray Imaging Facility, School of Materials, The University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom
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Karimi D, Ward RK. Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography. Int J Comput Assist Radiol Surg 2016; 11:1765-77. [PMID: 27287761 DOI: 10.1007/s11548-016-1434-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 05/27/2016] [Indexed: 11/28/2022]
Abstract
PURPOSE Image models are central to all image processing tasks. The great advancements in digital image processing would not have been made possible without powerful models which, themselves, have evolved over time. In the past decade, "patch-based" models have emerged as one of the most effective models for natural images. Patch-based methods have outperformed other competing methods in many image processing tasks. These developments have come at a time when greater availability of powerful computational resources and growing concerns over the health risks of the ionizing radiation encourage research on image processing algorithms for computed tomography (CT). The goal of this paper is to explain the principles of patch-based methods and to review some of their recent applications in CT. METHODS We first review the central concepts in patch-based image processing and explain some of the state-of-the-art algorithms, with a focus on aspects that are more relevant to CT. Then, we review some of the recent application of patch-based methods in CT. RESULTS Patch-based methods have already transformed the field of image processing, leading to state-of-the-art results in many applications. More recently, several studies have proposed patch-based algorithms for various image processing tasks in CT, from denoising and restoration to iterative reconstruction. Although these studies have reported good results, the true potential of patch-based methods for CT has not been yet appreciated. CONCLUSIONS Patch-based methods can play a central role in image reconstruction and processing for CT. They have the potential to lead to substantial improvements in the current state of the art.
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Affiliation(s)
| | - Rabab K Ward
- , 2366 Main Mall, Vancouver, BC, V6T 1Z4, Canada
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Kazantsev D, Guo E, Kaestner A, Lionheart WRB, Bent J, Withers PJ, Lee PD. Temporal sparsity exploiting nonlocal regularization for 4D computed tomography reconstruction. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2016; 24:207-219. [PMID: 27002902 PMCID: PMC4929339 DOI: 10.3233/xst-160546] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Revised: 12/14/2015] [Accepted: 02/07/2016] [Indexed: 06/05/2023]
Abstract
X-ray imaging applications in medical and material sciences are frequently limited by the number of tomographic projections collected. The inversion of the limited projection data is an ill-posed problem and needs regularization. Traditional spatial regularization is not well adapted to the dynamic nature of time-lapse tomography since it discards the redundancy of the temporal information. In this paper, we propose a novel iterative reconstruction algorithm with a nonlocal regularization term to account for time-evolving datasets. The aim of the proposed nonlocal penalty is to collect the maximum relevant information in the spatial and temporal domains. With the proposed sparsity seeking approach in the temporal space, the computational complexity of the classical nonlocal regularizer is substantially reduced (at least by one order of magnitude). The presented reconstruction method can be directly applied to various big data 4D (x, y, z+time) tomographic experiments in many fields. We apply the proposed technique to modelled data and to real dynamic X-ray microtomography (XMT) data of high resolution. Compared to the classical spatio-temporal nonlocal regularization approach, the proposed method delivers reconstructed images of improved resolution and higher contrast while remaining significantly less computationally demanding.
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Affiliation(s)
- Daniil Kazantsev
- The Manchester X-ray Imaging Facility, School of Materials, The University of Manchester, Manchester, UK
- Research Complex at Harwell, Didcot, Oxfordshire, UK
| | - Enyu Guo
- The Manchester X-ray Imaging Facility, School of Materials, The University of Manchester, Manchester, UK
- Research Complex at Harwell, Didcot, Oxfordshire, UK
| | - Anders Kaestner
- Neutron Imaging and Activation Group, Paul Scherrer Institut, Switzerland
| | | | | | - Philip J. Withers
- The Manchester X-ray Imaging Facility, School of Materials, The University of Manchester, Manchester, UK
- Research Complex at Harwell, Didcot, Oxfordshire, UK
| | - Peter D. Lee
- The Manchester X-ray Imaging Facility, School of Materials, The University of Manchester, Manchester, UK
- Research Complex at Harwell, Didcot, Oxfordshire, UK
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Van Eyndhoven G, Batenburg KJ, Kazantsev D, Van Nieuwenhove V, Lee PD, Dobson KJ, Sijbers J. An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2015; 24:4446-4458. [PMID: 26259219 DOI: 10.1109/tip.2015.2466113] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing, and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique. In this paper, a new iterative CT reconstruction algorithm for improved a temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. First, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Second, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary. Quantitative and qualitative results on different simulation experiments and a real neutron tomography data set show that, in comparison with the state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, the temporal resolution can be substantially increased, and thus fluid flow experiments with faster dynamics can be performed.
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Atwood RC, Bodey AJ, Price SWT, Basham M, Drakopoulos M. A high-throughput system for high-quality tomographic reconstruction of large datasets at Diamond Light Source. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2015; 373:rsta.2014.0398. [PMID: 25939626 PMCID: PMC4424489 DOI: 10.1098/rsta.2014.0398] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Tomographic datasets collected at synchrotrons are becoming very large and complex, and, therefore, need to be managed efficiently. Raw images may have high pixel counts, and each pixel can be multidimensional and associated with additional data such as those derived from spectroscopy. In time-resolved studies, hundreds of tomographic datasets can be collected in sequence, yielding terabytes of data. Users of tomographic beamlines are drawn from various scientific disciplines, and many are keen to use tomographic reconstruction software that does not require a deep understanding of reconstruction principles. We have developed Savu, a reconstruction pipeline that enables users to rapidly reconstruct data to consistently create high-quality results. Savu is designed to work in an 'orthogonal' fashion, meaning that data can be converted between projection and sinogram space throughout the processing workflow as required. The Savu pipeline is modular and allows processing strategies to be optimized for users' purposes. In addition to the reconstruction algorithms themselves, it can include modules for identification of experimental problems, artefact correction, general image processing and data quality assessment. Savu is open source, open licensed and 'facility-independent': it can run on standard cluster infrastructure at any institution.
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Affiliation(s)
- Robert C Atwood
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0QX, UK
| | - Andrew J Bodey
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0QX, UK
| | - Stephen W T Price
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0QX, UK
| | - Mark Basham
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0QX, UK
| | - Michael Drakopoulos
- Diamond Light Source Ltd, Harwell Science and Innovation Campus, Didcot OX11 0QX, UK
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